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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1684281101 PR_kwDOAMm_X85PKWFR 7787 Allow the label run-upstream to run upstream CI Illviljan 14371165 closed 0     6 2023-04-26T05:07:44Z 2024-03-13T21:44:50Z 2023-05-01T19:12:26Z MEMBER   0 pydata/xarray/pulls/7787

This PR makes it easier to trigger the upstream CI and creates a mypy test for upstream as well, but only when adding this label so that it doesn't mess with the scheduled runs and those results.

Noticed the need in #7786.

 
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     xarray 13221727 pull
1420242462 PR_kwDOAMm_X85BYlCD 7204 absolufy-imports - No relative imports - PEP8 Illviljan 14371165 closed 0     6 2022-10-24T05:27:24Z 2022-12-10T11:42:43Z 2022-12-07T21:24:44Z MEMBER   0 pydata/xarray/pulls/7204

I saw dask has started using absolute imports in https://github.com/dask/dask/pull/8796. I find it much more readable and there's a nice pre-commit for it as well.

Easiest way to deal with the merge conflicts is probably to just accept your changes and let pre-commit fix them afterwards.

 
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1420230433 PR_kwDOAMm_X85BYia0 7203 Avoid loading any data for reprs Illviljan 14371165 closed 0     6 2022-10-24T05:12:40Z 2022-10-28T16:23:19Z 2022-10-28T16:23:19Z MEMBER   0 pydata/xarray/pulls/7203
  • [x] Closes #6722
  • [x] Tests added
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
 
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1376095009 PR_kwDOAMm_X84_G4FM 7044 Self should be Any Illviljan 14371165 closed 0     6 2022-09-16T15:18:21Z 2022-09-23T18:56:40Z 2022-09-16T15:54:54Z MEMBER   0 pydata/xarray/pulls/7044

Self should be as permissive as possible until it works properly.

Using Any instead of TypeVar avoids errors like these: C:\users\j.w\documents\github\xarray\xarray\plot\facetgrid.py:462: error: Incompatible return value type (got "FacetGrid", expected "Self")

xref: #6923

 
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1124884287 PR_kwDOAMm_X84yHhLI 6240 Run pyupgrade on core/utils Illviljan 14371165 closed 0     6 2022-02-05T09:39:48Z 2022-08-12T09:08:36Z 2022-02-05T21:29:37Z MEMBER   0 pydata/xarray/pulls/6240

Make #6239 cleaner by running pyupgrade separately.

pyupgrade fixes typing only if from __future__ import annotations has been manually added to the file. Could probably do this in other files as well.

xref: #6244

 
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990194656 MDExOlB1bGxSZXF1ZXN0NzI4ODE5ODk1 5772 Create benchmark for groupby Illviljan 14371165 closed 0     6 2021-09-07T17:23:50Z 2022-08-12T09:03:28Z 2021-09-20T20:15:30Z MEMBER   0 pydata/xarray/pulls/5772
  • [x] Benchmark from #659
  • [x] Tests added
  • [x] Passes pre-commit run --all-files
 
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976179483 MDExOlB1bGxSZXF1ZXN0NzE3MTgwODkw 5726 Bump minimum version requirements for dask from 2.15 to 2.24 Illviljan 14371165 closed 0     6 2021-08-21T17:07:10Z 2021-09-08T17:58:26Z 2021-08-25T21:08:01Z MEMBER   0 pydata/xarray/pulls/5726

Dask released 2.24 around august 22 2020: https://github.com/dask/dask/releases/tag/2.24.0

  • [x] May help fixing errors in #5571
  • [x] Passes pre-commit run --all-files
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
 
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895713502 MDExOlB1bGxSZXF1ZXN0NjQ3OTAwMzEy 5345 Remove npcompat.moveaxis Illviljan 14371165 closed 0     6 2021-05-19T17:41:31Z 2021-07-02T16:08:17Z 2021-06-17T17:18:28Z MEMBER   0 pydata/xarray/pulls/5345

Remove some compatibility code that doesn't seem necessary anymore.

  • [x] Passes pre-commit run --all-files
 
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     xarray 13221727 pull
778123296 MDExOlB1bGxSZXF1ZXN0NTQ4MjY4ODg5 4762 Print number of variables in repr Illviljan 14371165 closed 0     6 2021-01-04T14:13:17Z 2021-05-18T18:17:50Z 2021-01-12T00:21:19Z MEMBER   0 pydata/xarray/pulls/4762

Show the printed and total number of variables in the repr.

  • [x] Passes isort . && black . && mypy . && flake8

```python import numpy as np import xarray as xr

a = np.arange(0, 15) b = np.core.defchararray.add("long_variable_name", a.astype(str)) c = np.arange(0, 25) d = np.core.defchararray.add("attr_", c.astype(str)) e = {k: 2 for k in d} coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=np.array([0, 1]).astype(bool), dims=["time"], coords=coords, ) ds1 = xr.Dataset(data_vars) ds1.attrs = e

The repr now shows how many attributes in total there are:

print(ds1) Out[10]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: (12/15) long_variable_name0 (time) bool False True long_variable_name1 (time) bool False True long_variable_name2 (time) bool False True long_variable_name3 (time) bool False True long_variable_name4 (time) bool False True long_variable_name5 (time) bool False True ... long_variable_name9 (time) bool False True long_variable_name10 (time) bool False True long_variable_name11 (time) bool False True long_variable_name12 (time) bool False True long_variable_name13 (time) bool False True long_variable_name14 (time) bool False True Attributes: (12/25) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_19: 2 attr_20: 2 attr_21: 2 attr_22: 2 attr_23: 2 attr_24: 2 ```

 
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     xarray 13221727 pull

Advanced export

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